首页> 外文期刊>Biotechnology Journal: Healthcare,Nutrition,Technology >Inclusion of mPRISM potential for polymer-induced protein interactions enables modeling of second osmotic virial coefficients in aqueous polymer-salt solutions
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Inclusion of mPRISM potential for polymer-induced protein interactions enables modeling of second osmotic virial coefficients in aqueous polymer-salt solutions

机译:包含聚合物诱导的蛋白质相互作用的mPRISM潜力,可以对聚合物盐水溶液中的第二渗透病毒系数进行建模

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摘要

The downstream processing of therapeutic proteins is a challenging task. Key information needed to estimate applicable workup strategies (e.g. crystallization) are the interactions of the proteins with other components in solution. This information can be deduced from the second osmotic virial coefficient B-22, measurable by static light scattering. Thermodynamic models are very valuable for predicting B-22 data for different process conditions and thus decrease the experimental effort. Available B-22 models consider aqueous salt solutions but fail for the prediction of B-22 if an additional polymer is present in solution. This is due to the fact that depending on the polymer concentration protein-protein interactions are not rectified as assumed within these models. In this work, we developed an extension of the xDLVO model to predict B-22 data of proteins in aqueous polymer-salt solutions. To show the broad applicability of the model, lysozyme, gamma-globulin and D-xylose ketol isomerase in aqueous salt solution containing polyethylene glycol were considered. For all proteins considered, the modified xDLVO model was able to predict the experimentally observed non-monotonical course in B-22 data with high accuracy. When used in an early stage in process development, the model will contribute to an efficient and cost effective downstream processing development.
机译:治疗性蛋白质的下游加工是一项艰巨的任务。估计适用的后处理策略(例如结晶)所需的关键信息是蛋白质与溶液中其他成分的相互作用。该信息可以从第二渗透维里系数B-22推导出来,该系数可以通过静态光散射来测量。热力学模型对于预测不同工艺条件下的B-22数据非常有价值,因此可以减少实验工作。可用的B-22模型考虑了盐水溶液,但如果溶液中存在其他聚合物,则无法预测B-22。这是由于以下事实:根据聚合物浓度的不同,蛋白质-蛋白质之间的相互作用无法像这些模型中所假设的那样得到纠正。在这项工作中,我们开发了xDLVO模型的扩展,以预测聚合物盐水溶液中蛋白质的B-22数据。为了显示该模型的广泛适用性,考虑了在含有聚乙二醇的盐水溶液中的溶菌酶,γ-球蛋白和D-木糖酮醇异构酶。对于所有考虑的蛋白质,改进的xDLVO模型都可以高精度预测B-22数据中实验观察到的非单调过程。当在流程开发的早期阶段使用时,该模型将有助于高效和经济高效的下游流程开发。

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